Research Article
Machine Learning-Based Optimization and Anomaly Detection in Software Defined Networks
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 53 |
| Published: November 2025 |
| Authors: Aastik Sharma |
10.5120/ijca2025925904
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Aastik Sharma . Machine Learning-Based Optimization and Anomaly Detection in Software Defined Networks. International Journal of Computer Applications. 187, 53 (November 2025), 42-46. DOI=10.5120/ijca2025925904
@article{ 10.5120/ijca2025925904,
author = { Aastik Sharma },
title = { Machine Learning-Based Optimization and Anomaly Detection in Software Defined Networks },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 53 },
pages = { 42-46 },
doi = { 10.5120/ijca2025925904 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Aastik Sharma
%T Machine Learning-Based Optimization and Anomaly Detection in Software Defined Networks%T
%J International Journal of Computer Applications
%V 187
%N 53
%P 42-46
%R 10.5120/ijca2025925904
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Software Defined Networks (SDN) enhance network programmability by separating the control and data planes, yet challenges remain in performance, traffic optimization, and security. This paper evaluates the integration of machine learning (ML) techniques, including K-Nearest Neighbor, Decision Tree, Support Vector Machine, Bayesian models, and Deep Neural Networks, to improve SDN performance. Experiments across multiple scenarios demonstrate that ML algorithms can enhance traffic prediction, detect anomalies, and mitigate DDoS attacks, achieving up to 100% accuracy in specific configurations. The study highlights the potential of ML to significantly improve SDN efficiency, security, and scalability.
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